Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm

Purpose:Diagenetic modelling, the mathematical simulation of the breakdown of sedimentary organic matter and subsequent fate of associated nutrients, has progressed to a point where complex, non-steady state environments can be accurately modelled. A genetic algorithm has never been used in conjunction with an early diagenetic model, and so we aim to discover whether this method is viable to determining a set of realistic model parameters, which itself is often a difficult task.Materials and methods:A range of sensitivity analyses were conducted to establish the parameters for which the model was most sensitive before a micro-genetic algorithm (?GA) was used to fit an output from a previously published diagenetic model (OMEXDIA) to observational data, taken at the North Dogger site from a series of cruises in the North Sea. Profiles of carbon, oxygen, nitrate and ammonia were considered. The method allows a set of parameters to be determined in a manner analogous to natural selection. Each iteration of the genetic algorithm within each experiment decreases the variance between the observed profiles and those calculated by OMEXDIA.Results and discussion:Despite some of the observed profiles, particularly for carbon, showing unusual patterns, the genetic algorithm was able to generate a set of parameters which was able to fit the observations. The genetic algorithm can therefore help to determine the values of other parameters used in the model, for which observational values are difficult to measure (e.g. the flux of organic matter to the sediment from the overlying water column and the rates of degradation of organic matter). We also show that the values of the parameters determined by the ?GA technique are able to be used in a potentially temporally predictive manner.Conclusions:The ?GA used is a viable method to fit carbon and nutrient sedimentary profiles observed in complex, dynamic shelf sea systems, despite OMEXDIA originally being designed for a different sedimentary environment. The results therefore show that this novel use of a genetic algorithm is a suitable method for both model calibration and validation and that the technique may help in explaining processes which are poorly understood

Diverse Metabolic Capacities of Fungi for Bioremediation

Bioremediation refers to cost-effective and environment-friendly method for converting the toxic, recalcitrant pollutants into environmentally benign products through the action of various biological treatments. Fungi play a major role in bioremediation owing to their robust morphology and diverse metabolic capacity. The review focuses on different fungal groups from a variety of habitats with their role in bioremediation of different toxic and recalcitrant compounds; persistent organic pollutants, textile dyes, effluents from textile, bleached kraft pulp, leather tanning industries, petroleum, polyaromatic hydrocarbons, pharmaceuticals and personal care products, and pesticides. Bioremediation of toxic organics by fungi is the most sustainable and green route for cleanup of contaminated sites and we discuss the multiple modes employed by fungi for detoxification of different toxic and recalcitrant compounds including prominent fungal enzymes viz., catalases, laccases, peroxidases and cyrochrome P450 monooxygeneses. We have also discussed the recent advances in enzyme engineering and genomics and research being carried out to trace the less understood bioremediation pathways